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Neural simulation Neural simulation The area of neural simulation began with the classic Hodgkin and Huxley model of the action potential (Hodgkin and Huxley 1952). At that point calculating a single action potential using a manually cranked calculator took 8 hours of hard manual work. Since then the ability to compute neural activity across large networks has developed enormously thanks to increases in computer power. What is needed?
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What information does it need for a given resolution? It is known that the morphology of neurons affects their spiking behaviour (Ascoli 1999), which suggests that neurons cannot simply be simulated as featureless cell bodies. 
In some cases simplifications of morphology can be done based on electrical properties (REF: Rall etc). One of the most important realisations of recent computational neuroscience in recent years is that neurons in themselves hold significant computational resources. Dendritic computing involves nonlinear interactions in the dendritic tree, allowing parts of neurons to act as ANNs on their own (Single and Borst 1998; London and Hausser 2005; Sidiropoulou, Pissadaki and Poirazi 2006). It appears possible that dendritic computation is a significant function that cannot be reduced into a whole-cell model but requires calculation of at least some neuron subsystems. Brain emulation need to take chemistry more into account than commonly occurs in current computational models (Thagard 2002).
Chemical processes inside neurons have computational power on their own and occur on a vast range of timescales (from sub-millisecond to weeks). Neuromodulators and hormones can change the causal structure of neural networks About 200 chemical species have been identified as involved in synaptic plasticity, forming a complex chemical network. However, much of the complexity may be redundant parallel implementations of a few core functions such as induction, pattern selectivity, expression of change, and maintenance of change (where the redundancy improves robustness and the possibility of fine-tuning) (Ajay and Bhalla 2006).
Proteomics methods are being applied to synapses, potentially identifying all present proteins (Li 2007). Synapse protein database At the very low numbers of molecules found in synaptic spines chemical noise becomes a significant factor, making chemical networks that are bistable at larger volumes unstable below the femtoliter level and reducing pattern selection (Bhalla 2004b; Bhalla 2004a). It is likely that complex formation or activity constrained by membranes is essential for the reliability of synapses.
In many species there exist identifiable neurons, neurons that can be distinguished from other neurons in the same animal and identified across individuals, and sets of equivalent cells that are mutually indistinguishable (but may have different receptive fields) (Bullock 2000). While relatively common in small and simple animals, identifiable neurons appear to be a minority in larger brains. Early animal brain emulations may make use of the equivalence by using data from several individuals, but as the brains become larger it is likely that all neurons have to be treated as individual and unique. Review of models signalling model collections Visual Data Mining of Brain Cs Visual Data Mining of Brain Cells (effect of morphology on functional properties) An issue that has been debated extensively is the nature of neural coding and especially whether neurons mainly make use of a rate code (where firing frequency contains the signal) or the exact timing of spikes matter (Rieke et al.